Recent research has focused on algorithms that can exploit the benefits of combining centralised training with decentralised execution [1]},
while mitigating some of the pathologies of vanilla centralized joint learning. A particularly successful line of research uses value factorisation of the joint \(Q\) -value function in order to reduce the size of the joint action space [VDN, QMIX, FacMADDPG][2]}, [3]}, [4]}.
